Impact of Categorical and Spatial Scale on Supervised Crop Classification using Remote Sensing

2015 ◽  
Vol 2015 (1) ◽  
pp. 7-20 ◽  
Author(s):  
Fabian Löw ◽  
Grégory Duveiller ◽  
Christopher Conrad ◽  
Ulrich Michel
Keyword(s):  
Remote Sensing ◽  
Spatial Scale ◽  
Crop Classification

scholarly journals An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images

2020 ◽  
Vol 13 (1) ◽  
pp. 65
Author(s):  
Jingtao Li ◽  
Yonglin Shen ◽  
Chao Yang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Short Term Memory ◽  
Complete Classification ◽  
Support Vector ◽  
Classification Models ◽  
Training Samples ◽  
Agricultural Applications ◽  
Crop Classification ◽  
Increasing Demand

Due to the increasing demand for the monitoring of crop conditions and food production, it is a challenging and meaningful task to identify crops from remote sensing images. The state-of the-art crop classification models are mostly built on supervised classification models such as support vector machines (SVM), convolutional neural networks (CNN), and long- and short-term memory neural networks (LSTM). Meanwhile, as an unsupervised generative model, the adversarial generative network (GAN) is rarely used to complete classification tasks for agricultural applications. In this work, we propose a new method that combines GAN, CNN, and LSTM models to classify crops of corn and soybeans from remote sensing time-series images, in which GAN’s discriminator was used as the final classifier. The method is feasible on the condition that the training samples are small, and it fully takes advantage of spectral, spatial, and phenology features of crops from satellite data. The classification experiments were conducted on crops of corn, soybeans, and others. To verify the effectiveness of the proposed method, comparisons with models of SVM, SegNet, CNN, LSTM, and different combinations were also conducted. The results show that our method achieved the best classification results, with the Kappa coefficient of 0.7933 and overall accuracy of 0.86. Experiments in other study areas also demonstrate the extensibility of the proposed method.

scholarly journals Remote sensing and ecosystem modeling to simulate terrestrial carbon fluxes

2019 ◽  
pp. 87
Author(s):  
Sergio Sánchez-Ruiz
Keyword(s):  
Remote Sensing ◽  
Carbon Cycle ◽  
Spatial Scale ◽  
Forest Ecosystems ◽  
Carbon Fluxes ◽  
Daily Basis ◽  
Ecosystem Models ◽  
Validation Process ◽  
Data Source ◽  
Peninsular Spain

<p>The main goal of this thesis is the establishment of a framework to analyze the forest ecosystems in peninsular Spain in terms of their role in the carbon cycle. In particular, the carbon fluxes that they exchange with atmosphere are modeled to evaluate their potential as carbon sinks and biomass reservoirs. The assessment of gross and net carbon fluxes is performed at 1-km spatial scale and on a daily basis using two different ecosystem models, Monteith and BIOME-BGC, respectively. These models are driven by a combination of satellite and ground data, part of the latter being also employed as a complementary data source and in the validation process.</p>

scholarly journals CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS

2016 ◽  
Vol XLI-B8 ◽  
pp. 1061-1066 ◽  
Author(s):  
Amit Kumar Verma ◽  
P. K. Garg ◽  
K. S. Hari Prasad ◽  
V. K. Dadhwal
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Vegetation Indices ◽  
Spectral Information ◽  
Main Concern ◽  
Crop Land ◽  
Sensing Applications ◽  
Crop Classification ◽  
Decision Tree Method ◽  
Tree Method

Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.

scholarly journals 3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

2018 ◽  
Vol 10 (2) ◽  
pp. 75 ◽  
Author(s):  
Shunping Ji ◽  
Chi Zhang ◽  
Anjian Xu ◽  
Yun Shi ◽  
Yulin Duan
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Remote Sensing Images ◽  
Multi Temporal ◽  
Crop Classification
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